Evaluation of a Hypertension Surveillance System, Kenema Government Hospital, Sierra Leone, 2021

This observational study assessed key attributes of the hypertension surveillance system at Kenema Government Hospital, Kenema District, Sierra Leone. We administered semistructured questionnaires; reviewed hospital registers, patient charts, and the District Health Information Software database; and rated the implementation status of each attribute as poor (1–3), average (4–6), or good (7–10). Of the 7 attributes, simplicity, flexibility, and acceptability were good; stability was average, but timeliness, sensitivity, and data quality were poor. Overall, the usefulness of the hypertension surveillance system was poor, as it did not monitor hypertension trends, nor was it linked to public health action.


Objective
Globally, in 2021, over 1 billion persons aged 30 to 79 years had hypertension (1), causing 7.5 million deaths (2). Sierra Leone, ranked 81 of 185 countries on the World Health Organization's ranking of deaths caused by hypertension in 2022, has an ageadjusted death rate of 20.3 persons per 100,000 population (3). The government of Sierra Leone developed a national policy and strategic plan in 2013 for the prevention and control of noncommunicable diseases, including hypertension (4). However, hypertension prevalence among adults aged 40 years or older increased from 44% in 2014 to 50% in 2020 (5,6). Hypertension is reported in the District Health Information Software (DHIS2), a national database platform launched in 2012 for reporting, analysis, and dissemination of data for several health programs, including the hypertension surveillance system. All health facilities are required to report routinely collected hypertension data to DHIS2 (7). The main diagnostic and treatment facility for hypertension in the eastern region of Sierra Leone is the Kenema Government Hospital (KGH). The hospital reports monthly data to the monitoring and evaluation officer, who reports to the DHIS2 (Figure). However, anecdotal reports indicated the KGH hypertension surveillance system was not providing representative data to the DHIS2, and the true burden of hypertension was not clear (8). Identifying the true burden of hypertension in Sierra Leone could guide the prevention, control, and management of hypertension. This study assessed key attributes of the surveillance system to determine if it met its objectives of estimating and monitoring the burden of hypertension.

Methods
We conducted a descriptive cross-sectional study at the KGH from January through July 2021. KGH has 392 clinicians and 350 beds serving a population of 670,000 (9). All persons with symptoms of hypertension at other peripheral health units are referred to KGH for medical attention. We purposefully selected and interviewed 195 health care workers who met the inclusion criteria of at least 6 months of experience in managing hypertension patients at the hospital and consenting to participate in the interview. We conducted the interviews in a private setting, and participants were informed that their responses would be kept confidential. We also collected demographic variables of the health care workers.
We used the Updated Guidelines for Evaluating Public Health Surveillance Systems from the US Centers for Disease Control and Prevention (CDC) (10) to assess the hypertension surveillance system's attributes. We used a semistructured questionnaire and observational techniques to collect qualitative data on usefulness, simplicity, flexibility, acceptability, and stability. We retrospectively reviewed and analyzed all available hospital registers, charts, and the DHIS2 database from January through December 2020 to collect quantitative data to assess data quality, sensitivity, and timeliness. We also explored independent data sources, in-cluding policies and regulations and gray literature from the Ministry of Health and Sanitation Sierra Leone websites to assess usefulness. We obtained permission from the hospital management to access the data from the hospital registers and charts.
We used Epi Info version 7 software (CDC) to compute the frequencies and proportions of demographic and other quantitative variables. We used evaluation criteria to rate the quality and implementation status of each attribute on a scale from 1 to 10. Poor was 1 to 3, average was 4 to 6, and good was 7 to 10. To compute the evaluation scores, we calculated the proportion of total respondents who answered each question. We then averaged the evaluation scores for each attribute. We reported the evaluation scores for quantifiable data.
We obtained ethical clearance from the Sierra Leone Ethics and Scientific Review Committee (SLESRC), and permission to access data at KGH was granted by hospital management upon receiving the SLESRC clearance. Hospital management and all participating health workers signed a written consent form. To ensure confidentiality, we encrypted the Excel file where the responses were saved and used only codes during data analysis.

Assessment of qualitative attributes
Simplicity was rated as good, with 68.7% (134 of 195) of respondents saying the case definition was simple to use in detecting hypertension cases. Regarding flexibility, the Ministry of Health and Sanitation introduced a new data tool for hypertension case reporting in September 2020. During our survey in 2021, 97.4% (190 of 195) of respondents stated that, although changes did occur in the case detection strategy, the changes did not affect the operation of the system. Therefore, flexibility was rated as good. Stability was rated as average, with 64.1% (125 of 195) of respondents saying they did not experience blood pressure monitors being out of stock. Acceptability was rated as good, as 100% (161 of 161) of records submitted by the deadline to the monitoring and evaluation officer were reported to the DHIS2. However, it was noted that the system relied on passive surveillance, which limited its ca-PREVENTING CHRONIC DISEASE pacity to detect and accurately monitor events over time and to identify any missing population groups in the entire district. A review of national, regional, and local rules, regulations, and policies, plus a gray literature review, identified no actions taken on data generated by the system ( Table 1).

Assessment of quantitative attributes
Timeliness was rated as poor; 78.8% (598 of 759) of records were not reported on time to the monitoring and evaluation officer. Sensitivity was rated as poor; the proportion of records submitted to the DHIS2 was 21.2% (161 of 759). Only follow-up cases were reported to DHIS2. Data quality was rated as poor; 4.5% (34 of 759) of records documented smoking history, 2.8% (21 of 759) reported weight, and no record documented physical activity and lipid profiles ( Table 2).

Discussion
Of the 7 attributes we assessed, data quality, sensitivity, and timeliness were poor; stability was average; and simplicity, flexibility, and acceptability were good. The overall usefulness of the KGH surveillance system was rated as poor, as it did not fully meet its objective to monitor trends and epidemiologic patterns of hypertension and was not linked to public health action.
The evaluation revealed that the sensitivity of the surveillance system was lower than that of the Ebola surveillance system (21.2% vs 88.5%) in a study conducted at the Tonkolili district, Sierra Leone (11). Because only follow-up cases were reported to the DHIS2, the true burden of hypertension, if estimated by using data generated from the DHIS2, might be a lower estimate. Even though the system was noted as simple for staff to use, the data quality was poor. Most of the known risk factors, such as smoking history, weight, physical activity, and lipid profiles, were not captured in hospital records. These missing data could possibly impair decision making or program planning for specific interventions. The lack of data could be due to a shortage of effective oversight by supervisors. The supervisors did not monitor the data process within the hospital, nor did they provide regular feedback to health staff on the system's performance. Although the system continuously functioned during the study period, the stability of the system was affected by the COVID-19 pandemic. Both human and financial resources were diverted to respond to COVID-19 prevention and control activities. This study also revealed that, even though KGH is the main referral hospital in 3 districts for persons presenting with symptoms of hypertension, the hypertension surveillance system's reliance on passive surveil-lance limits its capacity to detect the occurrence of cases at the community level. This implies that the system is not generating data that can reflect the true burden and pattern of hypertension in the population under surveillance.
The main limitations of the study included a purposefully selected sample for assessing the qualitative attributes. Also, because of the lack of available data we were not able to quantitatively assess the predictive value positive and representativeness of the surveillance system. Although everyone seeking care at KGH was triaged to improve patient health outcomes, the number of persons screened, including those referred by health workers or selfreferred, was unavailable.
When compared with communicable disease surveillance systems, only minimal support is provided to improve the hypertension surveillance system in many low-income countries, including Sierra Leone. Lack of funding for staff recruitment, training, and logistics presents a substantial challenge to the hypertension surveillance system's ability to generate data that are supported by evidence and used in public health.
In summary, the overall score for the surveillance system was poor. The disease burden could not be determined by using the generated data, and the trend and pattern of hypertension in KGH could not be monitored. The system's low sensitivity might have limited its capacity to detect, estimate, and monitor the burden of hypertension in the population. Its usefulness was limited as it fell short of its objectives and the data generated were not used for decision making or action. The general process of data collection and recordings should be expanded, and a supervision system should be put in place to strengthen the performance of the system. Therefore, we recommend that data collection, input of data into the system, and analysis of data be strengthened through increased hiring and training. Supervision should be increased to focus on entering and reporting data to the DHIS2.